Abstract: Systems and methods are provided for merging task graphs representing variants of an automatable task into a merged task graph. A plurality of task graphs each representing a variant of an automatable task is received. Similarities between the plurality of task graphs are identified. The plurality of task graphs is merged into a merged task graph of the automatable task based on the identified similarities. The merged task graph is output.

Abstract: Systems and methods are provided for determining variants of an automatable task. Task flow data of a performance of an automatable task by one or more users is received. The task flow data is generated based on user input using task mining. User interaction data is identified from the task flow data. One or more variants of the automatable task are determined based on the user interaction data using a machine learning based model. The one or more variants of the automatable task are output.

Abstract: Systems and methods for mapping interactive UI (user interface) elements to an RPA (robotic process automation) object repository are provided. User input selecting a window of an application displayed on a display device is received. In response to receiving the user input selecting the window of the application, interactive UI elements in the window of the application are automatically identified. User input selecting one or more of the identified interactive UI elements in the window of the application is received. The one or more selected interactive UI elements are stored in an RPA object repository of an RPA system.

Abstract: In some embodiments, a robotic process automation (RPA) robot is configured to identify a runtime target of an automation activity (e.g., a button to click, a form field to fill in, etc.) by searching a user interface for a UI element matching a set of characteristics of the target defined at design-time. When the target identification fails, some embodiments display an error message indicating which target characteristic could not be matched. Some embodiments further display for selection by the user a set of alternative target elements of the runtime interface.

Abstract: A facial recognition framework may be configured for robotic process automation (RPA) to automate a workflow for an application interface. A set of images of a user may be captured after the robot is initiated for the automated workflow. The set of images may be utilized by a deep learning neural network model to identify facial characteristics. The automated workflow may be performed by an attended robot based on successful validation of the user with the facial characteristics and credentials of the user for the attended robot.

Abstract: Artificial intelligence (AI) layer-based process extraction for robotic process automation (RPA) is disclosed. Data collected by RPA robots and/or other sources may be analyzed to identify patterns that can be used to suggest or automatically generate RPA workflows. These AI layers may be used to recognize patterns of user or business system processes contained therein. Each AI layer may “sense” different characteristics in the data and be used individually or in concert with other AI layers to suggest RPA workflows.

Abstract: Human-in-the-loop robot training using artificial intelligence (AI) for robotic process automation (RPA) is disclosed. This may be accomplished by a listener robot watching interactions of a user or another robot with a computing system. Based on the interactions by the user or robot with the computing system, the robot may be improved and/or personalized for the user or a group of users.

Abstract: A system and a method for performing a test of an application using an automation bot are provided. The method comprises accessing the application to be tested. The method comprises executing the test of the application using the automation bot. The automation bot is configured to interact with one or more other applications. The one or more other applications are different from the application. The method comprises determining one or more test results of the application based on the execution of the test. Further, the method comprises generating a notification indicative of the determined one or more test results.

Abstract: In some embodiments, a robotic process automation (RPA) design application provides a user-friendly graphical user interface that unifies the design of automation activities performed on desktop computers with the design of automation activities performed on mobile computing devices such as smartphones and wearable computers. Some embodiments connect to a model device acting as a substitute for an actual automation target device (e.g., smartphone of specific make and model) and display a model GUI mirroring the output of the respective model device. Some embodiments further enable the user to design an automation workflow by directly interacting with the model GUI.

Abstract: Dynamically updating, or retraining and updating, artificial intelligence (AI)/machine learning (ML) models in digital processes at runtime is disclosed. Production operation may not need to be stopped for AI/ML model update or retraining and update. The update steps and/or retraining steps for the AI/ML model may be included as part of the digital process. The AI/ML model update may be requested from internal logic (e.g., from the evaluation of a condition, by an expression that calls for the AI/ML model, etc.), external requests (e.g., from external triggers in a finite state machine (FSM), such as a file change, database data, a service call, etc.), or both. Automation of AI/ML model updates or retraining and updates may be provided, where the software reloads/reinitializes/re-instantiates with a retrained and/or updated AI/ML model after (and possibly immediately after) the AI/ML model becomes available.

Abstract: Disclosed herein is a method implemented by a task mining engine. The task mining engine is stored as processor executable code on a memory. The processor executable code is executed by a processor that is communicatively coupled to the memory. The method includes receiving recorded tasks identifying user activity with respect to a computing environment and clustering the recorded user tasks into steps by processing and scoring each recorded user task. The method also includes extracting step sequences that identify similar combinations or repeated combinations of the steps to mimic the user activity.

Abstract: Automation windows for RPA for attended or unattended robots are disclosed. A child session is created and hosted as a window including the UIs of applications of a window associated with a parent session. Running multiple sessions allows a robot to operate in this child session while the user interacts with the parent session. The user may thus be able to interact with applications that the robot is not using or the user and the robot may be able to interact with the same application if that application is capable of this functionality. The user and the robot are both interacting with the same application instances and file system. Changes made via the robot and the user in an application will be made as if a single user made them, rather than having the user and the robot each work with separate versions of the applications and file systems.

Abstract: A method and/or apparatus for creating and/or editing a machine pool with bring your own machine (BYOM) includes creating and/or editing a machine pool with a static list of machines. A user input machine list and an existing machine list are retrieved, and the user input machine list and existing machine list are compared to identify one or more changes between the user input machine list and existing machine list. Next, a new machine specification is created when the one or more changes between the user input machine list and existing machine list are identified. The one or more machines are then moved to the new machine specification.

Abstract: Process evolution for robotic process automation (RPA) and RPA workflow micro-optimization are disclosed. Initially, an RPA implementation may be scientifically planned, potentially using artificial intelligence (AI). Embedded analytics may be used to measure, report, and align RPA operations with strategic business outcomes. RPA may then be implemented by deploying AI skills (e.g., in the form of machine learning (ML) models) through an AI fabric that seamlessly applies, scales, manages AI for RPA workflows of robots. This cycle of planning, measuring, and reporting may be repeated, potentially guided by more and more AI, to iteratively improve the effectiveness of RPA for a business. RPA implementations may also be identified and implemented based on their estimated return on investment (ROI).

Abstract: A method is implemented by a controller executed on at least one processor. The method provides pre-authorized access to a robotic process automation for a resource associated with a job. The method includes causing, by the controller, the robotic process automation to assume a user identity during an authentication flow to enable access by the robotic process automation to a resource. The method includes issuing, by the controller, tokens to the robotic process automation during the authentication flow. The method includes enabling, by the controller via the tokens, the identity service that governs the resource to participate in operations of the controller to provide the pre-authorized access to the robotic process automation.

Abstract: A method for visualizing a process map is executed by a process map server. The method includes receiving a flowchart and a step-by-step recording related to a process. Generating a process map by combining the flowchart and the step-by-step recording and displaying the process map. The process map displays a task, step, and action related to the process. A detail window shows information associated with the process, and portions of the process, in response to user input. The action is based on information from the step-by-step recording.

Abstract: A computing device may execute a robot service that receives process requests to store in a process queue in memory. The robot service may utilize user-defined preferences to prioritize the process requests in the process queue. The process requests may be scheduled based on the user-defined preferences. The robot service may initiate the scheduled process requests for robotic automation of the application.

Abstract: Probabilistic models may be used in a deterministic workflow for robotic process automation (RPA). Machine learning (ML) introduces a probabilistic framework where the outcome is not deterministic, and therefore, the steps are not deterministic. Deterministic workflows may be mixed with probabilistic workflows, or probabilistic activities may be inserted into deterministic workflows, in order to create more dynamic workflows. A supervisor system may be used to monitor an ML model and raise an alarm, disable an RPA robot, bypass an RPA robot, or roll back to a previous version of the ML model when an error is detected by a data drift detector, a concept drift detector, or both.

Abstract: User interface (UI) object descriptors, UI object libraries, UI object repositories, and UI object browsers for robotic process automation (RPA) are disclosed. A UI object browser may be used for managing, reusing, and increasing the reliability of UI descriptors in a project. UI descriptors may be added to UI object libraries and be published or republished as UI object libraries for global reuse in a UI object repository. The UI object browser, UI object libraries, and UI object repository may facilitate reusability of UI element identification frameworks and derivatives thereof.

Abstract: According to one or more embodiments, a method executed by a type cache service implemented as a computer program within a computing environment is provided. The method includes scanning feeds publishing unpacked packages and automatically detecting new activities on the feeds. The method includes indexing the new activities and all previously detected activities according to type and generating a type cache for the unpacked packages according to the indexing of the new activities and all previously detected activities.